Recommended characteristics and processes for writing lay summaries of healthcare evidence: a co-created scoping review and consultation exercise
Bibliographic record
Abstract
Abstract Background Lay summaries (LSs) of scientific evidence are critical to sharing research with non-specialist audiences. This scoping review with a consultation exercise aimed to (1) Describe features of the available LS resources; (2) Summarize recommended LS characteristics and content; (3) Outline recommended processes to write a LS; and (4) Obtain stakeholder perspectives on LS characteristics and writing processes. Methods This project was a patient and public partner (PPP)-initiated topic co-led by a PPP and a researcher. The team was supported by three additional PPPs and four researchers. A search of peer-reviewed (Ovid MEDLINE, Scopus, Embase, Cochrane libraries, CINAHL, PsycINFO, ERIC and PubMed data bases) and grey literature was conducted using the Joanna Briggs Institute Methodological Guidance for Scoping Reviews to include any resource that described LS characteristics and writing processes. Two reviewers screened and extracted all resources. Resource descriptions and characteristics were organized by frequency, and processes were inductively analyzed. Nine patient and public partners and researchers participated in three consultation exercise sessions to contextualize the review findings. Results Of the identified 80 resources, 99% described characteristics of a LS and 13% described processes for writing a LS. About half (51%) of the resources were published in the last two years. The most recommended characteristics were to avoid jargon (78%) and long or complex sentences (60%). The most frequently suggested LS content to include was study findings (79%). The key steps in writing a LS were doing pre-work, preparing for the target audience, writing, reviewing, finalizing, and disseminating knowledge. Consultation exercise participants prioritized some LS characteristics differently compared to the literature and found many characteristics oversimplistic. Consultation exercise participants generally supported the writing processes found in the literature but suggested some refinements. Conclusions Writing LSs is potentially a growing area, however, efforts are needed to enhance our understanding of important LS characteristics, create resources with and for PPPs, and develop optimal writing processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.526 | 0.685 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.045 | 0.028 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".